Strokes

Artificial-intelligence-driven discovery of prognostic biomarker for sarcopenia.

BACKGROUND: Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established.

The Route of Motor Recovery in Stroke Patients Driven by Exoskeleton-Robot-Assisted Therapy: A Path-Analysis.

: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evidence regarding which variables might favor a better outcome and how this can be modulated by other factors. : In this within-subject study, we evaluated the efficacy of a robot-assisted rehabilitation system in the recovery of upper limb functions. We...

Kinematic Assessment to Measure Change in Impairment during Active and Active-Assisted Type of Robotic Rehabilitation for Patients with Stroke.

Analysis of kinematic features related to clinical assessment scales may qualitatively improve the evaluation of upper extremity movements of stroke p...

Circulating Levels of Endothelin-1 and Big Endothelin-1 in Patients with Essential Hypertension.

The role of endothelin-1 (ET-1) in the pathogenesis of hypertension (HTN) is not clearly established. There is evidence that its circulating levels ar...

Preoperative soluble cluster of differentiation 40 ligand level is associated with outcome of pulmonary endarterectomy.

OBJECTIVE: Soluble CD40 ligand (sCD40L) is associated with some pathobiological states. However, whether sCD40L in patients with chronic thromboemboli...

Elevated Plasma von Willebrand Factor Levels Are Associated With Subsequent Ischemic Stroke in Persons With Treated HIV Infection.

BACKGROUND: We assessed whether key biomarkers of endothelial activation and hemostasis/thrombosis were elevated in individuals receiving effective an...

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic al...

A deep learning-based model for prediction of hemorrhagic transformation after stroke.

Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' qu...

Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24.

OBJECTIVE: Previous studies have shown that excess weight (including obesity and overweight) can increase the risk of cardiovascular, cerebrovascular ...

Retrospective study of deep learning to reduce noise in non-contrast head CT images.

PURPOSE: Presented herein is a novel CT denoising method uses a skip residual encoder-decoder framework with group convolutions and a novel loss funct...

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.

OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for ...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims...

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to un...

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method.

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and manageme...

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorr...

A comparative study of anticoagulant/antiplatelet therapy among men undergoing robot-assisted radical prostatectomy: a prospective single institution study.

The present study aimed to assess the safety and efficacy of robot-assisted radical prostatectomy (RARP) in patients with prostate cancer (PCa) under ...

Automated Collateral Flow Assessment in Patients with Acute Ischemic Stroke Using Computed Tomography with Artificial Intelligence Algorithms.

BACKGROUND: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) ...

Home-based self-help telerehabilitation of the upper limb assisted by an electromyography-driven wrist/hand exoneuromusculoskeleton after stroke.

BACKGROUND: Most stroke survivors have sustained upper limb impairment in their distal joints. An electromyography (EMG)-driven wrist/hand exoneuromus...

ECG data dependency for atrial fibrillation detection based on residual networks.

Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detect...